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As power systems mature into smart grid entities, they face new challenges toward online monitoring and control of the system's behaviour. Burgeoning classes of cyber-attacks are observed which may cause instability of the power grid and system blackouts if not identified. In this study, the authors propose an ensemble Kalman filter based anomaly detector using a relaxation-based solution. Performance of the proposed method is tested with Chi-Square detector and Largest Normalised Residual test. Results of simulations based on real-world data, up to 5000 bus system, demonstrate the effectiveness of the proposed framework over traditional bad data detection in presence of false data injection attack.